Development and validation of an integrated machine learning model for recurrence-free survival prediction in clear cell renal cell carcinoma
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Le résumé fourni par la source
BACKGROUND: Integrated multimodal systems improve recurrence-free survival (RFS) prediction in surgically resected clear cell renal cell carcinoma (ccRCC) to overcome traditional model limitations. METHODS: A total of 1284 patients were enrolled and divided into training (n = 738), internal validation (n = 317), and external test (n = 229). A machine learning (ML)-based model combined clinical data, radiomics signature (Rad-sign), and deep learning signature (DL-sign) to predict recurrence at 3-, 5-, and 7-year post-surgery. Radiomics features were extracted from CT images using ML, with the Rad-sign showing the best performance. A three-dimensional vision transformer (3D-ViT) for volumetric feature extraction outperformed ResNet models. RESULTS: Cox regression identified clinical features associated with RFS (p < 0.05). An optimal clinical-DL nomogram was chosen from 8 ML algorithms via 5-fold cross-validation. Clinical features alone had limited predictive power (external test AUC: 0.576, 95% CI: 0.472-0.672), while radiomics modeled by Naive Bayes (NB) performed better (AUC: 0.728). Rad-sign's C-index was 0.729, and the 3D-ViT model achieved an external test AUC of 0.846. The nomogram's AUCs for predicting 3, 5, and 7-year RFS post-surgery were 0.920, 0.935, and 0.938, with C-indices of 0.890 and 0.910, respectively. It effectively stratified risk groups, outperforming UISS and SSIGN via calibration and decision curve analysis. CONCLUSION: The ML-based clinical-DL model shows promise for accurate ccRCC RFS prediction, which may aid in personalized risk assessment and decision-making.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Development and validation of an integrated machine learning model for recurrence-free survival prediction in clear cell renal cell carcinoma
- Date Crossref
- 01/10/2026
- Éditeur
- Elsevier BV
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Xinjiang Medical University VIP Department pays non établi dans la noticeUniversité ou école supérieure
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Tumor Hospital of Xinjiang Medical University pays non établi dans la noticeÉtablissement de santé
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The Central Hospital of Shaoyang Department of Urology pays non établi dans la noticeÉtablissement de santé
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Hunan Normal University Hunan Provincial People's Hospital pays non établi dans la noticeUniversité ou école supérieure
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Hunan Provincial People's Hospital pays non établi dans la noticeÉtablissement de santé
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First Affiliated Hospital of Xinjiang Medical University pays non établi dans la noticeÉtablissement de santé
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People's Hospital of Xinjiang Uygur Autonomous Region Tumor Center pays non établi dans la noticeÉtablissement de santé
VIP Department — Xinjiang Medical University, Tumor Hospital of Xinjiang Medical University et Department of Urology — The Central Hospital of Shaoyang, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.